{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b269188-24a3-4ef5-82b7-948e86fae031",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/b4466384f3bb4cc1a0bd02e88edb266f\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2012.cache... 9275 ima\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.18 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp84/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp84\u001b[0m\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        0/9      3.64G    0.07245    0.04723    0.06384         74        640: 1\n",
      "tensor([1.67885], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.912     0.0557     0.0792     0.0369\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        1/9      6.22G    0.05298    0.03986    0.05326        106        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5/': Failed to connect to github.com port 443 after 130532 ms: Connection timed out\n",
      "        1/9      6.22G    0.05105     0.0376    0.04525         82        640: 1\n",
      "tensor([1.39052], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.462      0.275      0.215      0.106\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        2/9      6.22G     0.0478    0.03564    0.03557         64        640: 1\n",
      "tensor([1.10651], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.417      0.426      0.375      0.177\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        3/9      6.22G    0.04561     0.0355    0.02821         54        640: 1\n",
      "tensor([1.05317], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.536       0.51      0.504      0.262\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        4/9      6.22G    0.04388    0.03498    0.02416         62        640: 1\n",
      "tensor([1.11286], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.569      0.554      0.561      0.306\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        5/9      6.22G    0.04188    0.03427    0.02096         62        640: 1\n",
      "tensor([1.04698], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.609      0.582      0.592      0.332\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        6/9      6.22G    0.04029    0.03384    0.01901         70        640: 1\n",
      "tensor([1.14311], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.651      0.609      0.643      0.373\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        7/9      6.22G    0.03845    0.03262    0.01686         57        640: 1\n",
      "tensor([1.03263], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.682      0.645      0.687       0.41\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        8/9      6.22G    0.03679    0.03196    0.01484         82        640: 1\n",
      "tensor([1.18045], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.687      0.655      0.702       0.43\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        9/9      6.22G    0.03508     0.0313    0.01338         52        640: 1\n",
      "tensor([0.83633], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.693      0.677      0.716      0.448\n",
      "\n",
      "10 epochs completed in 0.237 hours.\n",
      "Optimizer stripped from runs/train/exp84/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/exp84/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/exp84/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.692      0.678      0.715      0.448\n",
      "                   car       4952       1201      0.784      0.865      0.878      0.623\n",
      "                person       4952       4528      0.818      0.785      0.858      0.526\n",
      "             aeroplane       4952        285      0.882      0.681      0.806      0.467\n",
      "               bicycle       4952        337      0.789      0.742       0.81      0.497\n",
      "                  bird       4952        459      0.658       0.58      0.619      0.363\n",
      "                  boat       4952        263      0.536      0.544      0.522      0.274\n",
      "                bottle       4952        469      0.632      0.642      0.668      0.403\n",
      "                   bus       4952        213      0.767      0.779      0.819      0.608\n",
      "                   cat       4952        358      0.802      0.673      0.776      0.504\n",
      "                 chair       4952        756      0.536      0.603      0.593      0.355\n",
      "                   cow       4952        244      0.646      0.689      0.713      0.477\n",
      "           diningtable       4952        206      0.663       0.62      0.676      0.402\n",
      "                   dog       4952        489      0.702      0.587      0.693      0.409\n",
      "                 horse       4952        348      0.718      0.767      0.779      0.499\n",
      "             motorbike       4952        325      0.779      0.705        0.8      0.475\n",
      "           pottedplant       4952        480      0.513      0.465      0.432      0.189\n",
      "                 sheep       4952        242      0.518      0.744      0.667      0.461\n",
      "                  sofa       4952        239      0.636      0.664      0.684      0.461\n",
      "                 train       4952        282      0.828      0.683      0.787      0.491\n",
      "             tvmonitor       4952        308      0.632      0.747      0.727      0.482\n",
      "Results saved to \u001b[1mruns/train/exp84\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/b4466384f3bb4cc1a0bd02e88edb266f\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.7685132661176516\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 26.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8058004679005721\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.46717137120898633\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8818612730377436\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.6809844932651951\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 194.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.7645453622704552\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 67.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8103161701313614\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.4972768136753173\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.788684753515245\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7418397626112759\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 250.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.6161095035742549\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 138.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.6193487624380187\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.3631488590330656\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.6576298337207761\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.579520697167756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 266.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.5399743298585082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 124.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.5224913078624228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.27364738200973177\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.5362738481208814\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.5437262357414449\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 143.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6370444752467358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 175.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.6681353144070128\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.4026128621439316\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6323675997979403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6417910447761194\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 301.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.773185747440936\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8193200477721537\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.6079594615065019\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.7671252922148979\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.7793427230046949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 166.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8224648643142158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 287.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.8780893305904686\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.6229264323644297\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.7838245773971511\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.86511240632806\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1039.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.7320823225165\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 59.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.776373821349715\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.5042190232668647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8022745968559922\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.6731843575418994\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 241.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.5678655742290043\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 394.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.5932368931202898\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.3545333074508827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.5364675823887354\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6031673207599133\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 456.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.666496646730042\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 92.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.7125628502344008\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.47749914375978275\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.6458344863142464\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.6885245901639344\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 168.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6403536502565425\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.6764884045285261\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.4024967396661202\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.6625693124235871\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6195794205502942\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 128.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.6392008372066047\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 122.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.6927738692560336\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.40916814957909553\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.7017178039858097\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.5869120654396728\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 287.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.7415562740650572\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 105.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.7793252330314754\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.49887740268573466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.7175351935290244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.7672413793103449\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 267.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [571]                  : (0.9696003198623657, 3.8988897800445557)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [20]        : (0.07921218067525006, 0.7156086711240607)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [20]   : (0.03688005384803144, 0.44824310382861776)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [20]      : (0.41659137415484765, 0.9118115063336679)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [20]         : (0.055718386264936706, 0.6773290110878427)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.7399228787834546\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8002176678450513\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.47485149750200695\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.7789554721539119\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7046153846153846\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8007692650128821\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 793.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8577390977114189\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5263065495426142\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.817539579342372\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.7846731448763251\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3553.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.48768189988252103\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 212.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.4317837512607103\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.188745605712134\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.5131975091782285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.46458333333333335\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 223.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.6104441471275445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 168.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.6665376259323872\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.46072958309125517\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.5176362364236651\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.743801652892562\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 180.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.649701967735722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 91.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.6838771615407752\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.4605595853774923\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6356801075564826\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6643563701959191\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 159.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [20]         : (0.035082776099443436, 0.07245171815156937)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [20]         : (0.0133828641846776, 0.0638355165719986)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [20]         : (0.03129665553569794, 0.04723299667239189)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.7483970093730745\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 40.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.7872726593844138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.49107255079236767\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8279860073185513\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.682766986845001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 193.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.6844590901819184\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 134.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7271371413516189\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.4821754492343283\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.6318346318346318\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7466459966459967\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 230.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [20]           : (0.033707186579704285, 0.048562563955783844)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [20]           : (0.009776703082025051, 0.05045466125011444)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [20]           : (0.019010165706276894, 0.024346323683857918)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [20]                  : (0.00208, 0.07005172413793104)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [20]                  : (0.00208, 0.008015390804597702)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [20]                  : (0.00208, 0.008015390804597702)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/b4466384f3bb4cc1a0bd02e88edb266f\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BiC_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : 0.0001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp84\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.25 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 8 file(s), remaining 473.74 KB/3.52 MB\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC.yaml \\\n",
    "--epochs 10 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80fe57e8-8276-4e5c-a527-029a1919dfe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/exp84/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.844      0.639      0.711      0.422\n",
      "                   car       2244       8711      0.844      0.883      0.932      0.663\n",
      "                   van       2244        861      0.678      0.807      0.791      0.524\n",
      "                 truck       2244        333      0.873      0.829      0.893       0.59\n",
      "                  tram       2244        138      0.891      0.732      0.851      0.447\n",
      "                person       2244       1286      0.801      0.609      0.702      0.348\n",
      "        person_sitting       2244         89          1          0     0.0233     0.0118\n",
      "               cyclist       2244        496      0.804      0.669      0.748      0.377\n",
      "                  misc       2244        284       0.86      0.581      0.747      0.417\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp164\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/exp84/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9ec8e24-47b8-46f1-8dda-f22bc6677594",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec33138c-4fcf-472b-bd8b-b7d3ac0b4261",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434b3920-4f76-44d8-a48e-f6f96c899ada",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e8a1308-e8a2-41b7-b362-81cd33577875",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ac13baf-be10-41be-80d5-a3bad15d4f8f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/526c545c5ab247d3a333bf0f6f9eb953\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2012... 5717 images, 0\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/train2012.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.94 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp86/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp86\u001b[0m\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        0/9      3.64G    0.07605    0.04515    0.07168         19        640: 1\n",
      "tensor([0.64540], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.605     0.0668     0.0528      0.021\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        1/9      6.22G    0.05557    0.03871    0.05717         19        640: 1\n",
      "tensor([0.66942], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.436      0.182      0.128     0.0588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        2/9      6.22G     0.0516    0.03641    0.04694         30        640: 1\n",
      "tensor([0.64224], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.322      0.289      0.227       0.11\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        3/9      6.22G    0.04847    0.03567     0.0377         18        640: 1\n",
      "tensor([0.51887], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.354      0.393      0.314      0.153\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        4/9      6.22G    0.04672    0.03485    0.03214         23        640: 1\n",
      "tensor([0.55382], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.466      0.428      0.411      0.203\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        5/9      6.22G    0.04439    0.03484    0.02757         33        640: 1\n",
      "tensor([0.64326], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.559       0.53      0.541      0.285\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        6/9      6.22G    0.04259    0.03392    0.02515         15        640: 1\n",
      "tensor([0.36656], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.576      0.572      0.581      0.323\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        7/9      6.22G    0.04084    0.03307    0.02213         21        640: 1\n",
      "tensor([0.41012], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.599      0.584      0.598      0.332\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        8/9      6.22G    0.03864      0.032    0.01965         32        640: 1\n",
      "tensor([0.44400], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.651      0.608      0.643      0.376\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        9/9      6.22G    0.03692    0.03138    0.01811         21        640: 1\n",
      "tensor([0.36135], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.676      0.631      0.671      0.406\n",
      "\n",
      "10 epochs completed in 0.194 hours.\n",
      "Optimizer stripped from runs/train/exp86/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/exp86/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/exp86/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.677       0.63      0.671      0.406\n",
      "                   car       4952       1201      0.676      0.858      0.842      0.579\n",
      "                person       4952       4528      0.815      0.756      0.833      0.494\n",
      "             aeroplane       4952        285      0.861      0.604      0.739       0.41\n",
      "               bicycle       4952        337      0.789      0.691      0.775      0.456\n",
      "                  bird       4952        459      0.627      0.542      0.577      0.328\n",
      "                  boat       4952        263      0.563       0.49      0.515      0.276\n",
      "                bottle       4952        469      0.627      0.675      0.672      0.385\n",
      "                   bus       4952        213      0.734      0.709       0.78      0.584\n",
      "                   cat       4952        358      0.729      0.676      0.726      0.444\n",
      "                 chair       4952        756      0.481      0.487       0.43      0.245\n",
      "                   cow       4952        244      0.612      0.704      0.651      0.419\n",
      "           diningtable       4952        206      0.654       0.66      0.699      0.391\n",
      "                   dog       4952        489       0.71       0.44      0.612      0.367\n",
      "                 horse       4952        348      0.689       0.67      0.709      0.398\n",
      "             motorbike       4952        325      0.779      0.692      0.776       0.44\n",
      "           pottedplant       4952        480      0.483      0.373      0.346      0.154\n",
      "                 sheep       4952        242      0.553      0.752      0.721      0.477\n",
      "                  sofa       4952        239      0.643      0.485      0.571      0.367\n",
      "                 train       4952        282      0.853      0.667      0.745      0.446\n",
      "             tvmonitor       4952        308      0.664      0.679      0.709      0.462\n",
      "Results saved to \u001b[1mruns/train/exp86\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/526c545c5ab247d3a333bf0f6f9eb953\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.7096145490480573\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.7387561355805528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.4097353666041438\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8609891969020947\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.6035087719298246\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 172.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.7371296135501074\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 62.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.7747542513445632\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.45557415656584316\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.7893437897611563\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.6913946587537092\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 233.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.5817904667284858\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 148.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.5772956545996156\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.3280128599200166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.6272383548726957\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.5424836601307189\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 249.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.5240495348774821\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.5147638420599893\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.27602510950853304\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.5625330190642512\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.49049429657794674\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 129.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6503232206823775\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 188.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.6717121998711367\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.3847760570214782\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6274026187117496\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6749820182656002\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 317.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.7212576983431038\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 55.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.7803029851045521\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.5838951272589031\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.7340322400494141\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.7089201877934272\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 151.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.7558909125640634\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 494.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.8421427912864982\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.5793147303812355\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.6757373059418296\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8576186511240633\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1030.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.7015229264650591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 90.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.7255656376846571\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.44417894726510426\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.729074744432247\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.6759776536312849\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 242.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.48404433909294975\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 397.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.43003896675741105\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.24457916394871143\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.4813466011380896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.48677248677248675\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 368.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.6547180110851758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 109.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.6512202745500881\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.41930616014957434\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.6118187126713224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.7040869421333902\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 172.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6566466462120196\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 72.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.6990715068454741\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.3912696561808491\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.6536617657018995\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6596589120860966\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 136.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.5431755865984509\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 88.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.6122684248432035\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.3672106165601021\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.7104129461133374\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.4396728016359918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 215.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.6793289885614485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 105.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.7088385029213101\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.3978301442807947\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.6894082196372389\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.6695402298850575\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 233.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [357]                  : (1.126773476600647, 3.8243579864501953)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [20]        : (0.052788549026316155, 0.6714006876865783)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [20]   : (0.02100302136136107, 0.4061725916939106)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [20]      : (0.3217336866151879, 0.6761539850336362)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [20]         : (0.06683140910204853, 0.6306574154768181)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.733103202184268\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 64.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.7764252656998232\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.43997732446829235\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.7790076683350905\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.6923076923076923\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 225.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.7843117632517657\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 777.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8334652113285119\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.49421689680534786\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8149809037425136\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.7558671745950898\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3423.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.42085908303314457\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 192.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.34605274561420196\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.15409854031629133\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.4829470834570274\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.3729166666666667\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 179.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.6373880118211595\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 147.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.7211538102045933\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.47691216681408194\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.5530558097933985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7520661157024794\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 182.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.5530119042402085\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.571065560109774\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.3665434853677595\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6425852084947645\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.48535564853556484\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 116.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [20]         : (0.036918770521879196, 0.07604639232158661)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [20]         : (0.018108878284692764, 0.07167614251375198)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [20]         : (0.031381964683532715, 0.045153066515922546)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.748401266995922\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 32.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.7445393352897419\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.4460140146071393\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8529779896003805\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.6666666666666666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 188.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.671414988878437\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 106.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7090398287768687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.4622119289562465\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.6644079222281629\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.6785714285714286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 209.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [20]           : (0.035563018172979355, 0.05462392419576645)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [20]           : (0.011776561848819256, 0.05535700544714928)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [20]           : (0.019996147602796555, 0.02521480806171894)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [20]                  : (0.00208, 0.07008379888268157)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [20]                  : (0.00208, 0.008012532588454377)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [20]                  : (0.00208, 0.008012532588454377)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/526c545c5ab247d3a333bf0f6f9eb953\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BiC_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : 0.0001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp86\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.34 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC.yaml \\\n",
    "--epochs 10 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1238409d-3b5a-4d54-bea4-42ccf040889c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/exp86/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=True\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/f572840c990f403db35f2fbb970a21c6\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 354/355 items from runs/train/exp86/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning ../datasets/VOC/labels/val.cache... 3558 images, 0 backgrounds, \u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.45 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp87/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp87\u001b[0m\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        0/9     0.952G    0.04443    0.03803     0.0297         98        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "        0/9     0.952G    0.04429    0.03817    0.02924         45        640: 1\n",
      "tensor([0.64652], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.658      0.639      0.663      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        1/9      3.54G    0.04371    0.03811    0.02885         27        640: 1\n",
      "tensor([0.63626], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.656      0.638      0.661      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        2/9      3.54G    0.04471    0.03888    0.02942         46        640: 1\n",
      "tensor([0.68122], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.653      0.635      0.658      0.395\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        3/9      3.54G    0.04449    0.03857    0.02884         21        640: 1\n",
      "tensor([0.40446], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.657      0.638      0.661      0.396\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        4/9      3.54G    0.04499    0.03873    0.02841         31        640: 1\n",
      "tensor([0.58790], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.653      0.638      0.659      0.395\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        5/9      3.54G    0.04464    0.03883    0.02942         38        640: 1\n",
      "tensor([0.67467], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.66      0.635       0.66      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        6/9      3.54G    0.04495    0.03889     0.0293         34        640: 1\n",
      "tensor([0.64428], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.658      0.639      0.662      0.398\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        7/9      3.54G    0.04435    0.03844    0.02839         26        640: 1\n",
      "tensor([0.54091], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.659      0.638      0.663      0.399\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        8/9      3.54G    0.04474    0.03932     0.0289         52        640: 1\n",
      "tensor([0.72705], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.657      0.637      0.659      0.395\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        9/9      3.54G     0.0445    0.03884    0.02936         35        640: 1\n",
      "tensor([0.51529], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.659      0.639      0.662      0.398\n",
      "\n",
      "10 epochs completed in 0.149 hours.\n",
      "Optimizer stripped from runs/train/exp87/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/exp87/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/exp87/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.659      0.638      0.663      0.399\n",
      "                   car       4952       1201      0.773      0.806      0.844      0.576\n",
      "                person       4952       4528      0.808      0.753      0.831      0.491\n",
      "             aeroplane       4952        285      0.817      0.572      0.706      0.386\n",
      "               bicycle       4952        337      0.767      0.703      0.766      0.447\n",
      "                  bird       4952        459      0.581      0.582      0.564      0.318\n",
      "                  boat       4952        263      0.551      0.513      0.522      0.276\n",
      "                bottle       4952        469      0.603      0.677      0.656      0.373\n",
      "                   bus       4952        213      0.759      0.685      0.758      0.561\n",
      "                   cat       4952        358      0.668      0.732      0.717      0.443\n",
      "                 chair       4952        756      0.452      0.528      0.448      0.254\n",
      "                   cow       4952        244      0.611      0.672      0.646      0.412\n",
      "           diningtable       4952        206      0.643      0.648      0.679      0.367\n",
      "                   dog       4952        489      0.593      0.546      0.598      0.363\n",
      "                 horse       4952        348      0.697      0.716      0.731      0.414\n",
      "             motorbike       4952        325      0.708      0.687      0.732      0.412\n",
      "           pottedplant       4952        480      0.518      0.317      0.326      0.142\n",
      "                 sheep       4952        242      0.506      0.773      0.722      0.476\n",
      "                  sofa       4952        239      0.632      0.536      0.592      0.387\n",
      "                 train       4952        282      0.843       0.61       0.71      0.411\n",
      "             tvmonitor       4952        308      0.654      0.701       0.72      0.467\n",
      "Results saved to \u001b[1mruns/train/exp87\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/f572840c990f403db35f2fbb970a21c6\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.6728632604061575\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 36.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.7063331514766886\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.3862415147282764\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8170562995747742\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.5719298245614035\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 163.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.7336409434172414\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 72.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.7660180568976134\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.4469360290368141\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.7667604591751161\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7032640949554896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 237.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.581260912567604\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 193.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.5636736166612415\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.3180842270612664\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.5808231391380013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.5816993464052288\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 267.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.5313052431583695\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 110.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.5220467841214591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.2762459828862062\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.5506103726922542\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.5133079847908745\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 135.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.637821593065026\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 209.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.6560556612871477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.3734424476421711\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6030117858928619\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6768965127174084\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 317.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.7205481054016978\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 46.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.7578495392447869\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.5607962153245116\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.7594394492842487\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.6854460093896714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.7889732597292523\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 284.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.84371925987493\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.5759632270782207\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.7730658335363071\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8055490961402703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 967.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.6985816097930957\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 130.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.7172438360581944\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.4432374568853361\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.6682116853247806\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.7318435754189944\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 262.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.4871104857329501\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 483.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.4483030248358236\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.25350665466266264\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.4522619745345302\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5277777777777778\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 399.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.6401858509775687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 104.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.6457103116677305\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.41180789732317163\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.6111393929737158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.6721311475409836\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 164.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6456897065399867\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.6791465810460828\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.36729908401393047\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.6433626983143892\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6480336091986578\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 133.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.5688080169409668\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 183.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.5976985236194772\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.3634090782844226\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.5934031345955466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.5461705741187677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 267.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.7061390235304549\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 108.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.7311072197186863\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.414007082316485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.6970034640370931\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.7155172413793104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 249.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [223]                  : (0.6442839503288269, 2.3983848094940186)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [20]        : (0.6584085001305312, 0.6634476487993681)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [20]   : (0.39485927536381515, 0.39898961829935486)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [20]      : (0.6526801479696589, 0.6596923385540402)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [20]         : (0.634835398795253, 0.639025639365207)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.6971729385007138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 92.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.7315989707535238\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.41205709618142594\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.7080718612926771\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.6866044506044506\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 223.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.779582447098204\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 811.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.830711995608913\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.491210786581996\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8078865715567726\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.753194448865827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3410.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.39315515565787135\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 141.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.3258357315027019\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.14228680087775525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.5183615507034977\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.31666666666666665\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 152.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.6116470104798156\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 182.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.7221794282396029\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.47608084121050503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.5061389082629835\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7727272727272727\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 187.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.5797879921012016\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 75.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.592024895691718\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.3870566457444325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6319717247619633\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.5355648535564853\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 128.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [20]         : (0.04370786249637604, 0.04498838633298874)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [20]         : (0.02839343436062336, 0.029421387240290642)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [20]         : (0.038114551454782486, 0.03932066261768341)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.7076065952759812\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 32.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.710409994206823\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.41133978052607756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.842534940488073\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.6099290780141844\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 172.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.6768945548048397\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 114.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7195919861749975\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.46707453514833935\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.6541317477873368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7012987012987013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 216.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [20]           : (0.036148592829704285, 0.03637466952204704)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [20]           : (0.012590762227773666, 0.012713685631752014)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [20]           : (0.02020125836133957, 0.020352182909846306)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [20]                  : (0.00208, 0.07013452914798207)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [20]                  : (0.00208, 0.008008011958146488)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [20]                  : (0.00208, 0.008008011958146488)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/f572840c990f403db35f2fbb970a21c6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : 0.0001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp87\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.40 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC.yaml \\\n",
    "--epochs 10 \\\n",
    "--weights ./runs/train/exp86/weights/last.pt \\\n",
    "--BiC_enable\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74d77a8a-eda9-4db8-842c-4b7bbbaeb39f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7113c5cc-7777-46c4-965c-d2fc627f47d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15fb4f97-c03b-4acb-889f-772b8f6d09b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7722bf6-81c3-46cc-8e4d-15646c03579f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/increment_VOC_Replay_base/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=True\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/b11b71f0304a42f89a6b79335b29cf21\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 354/355 items from runs/train/increment_VOC_Replay_base/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning ../datasets/VOC/labels/val.cache... 3558 images, 0 backgrounds, \u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.45 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp88/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp88\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49     0.952G    0.02884    0.02675   0.005021         45        640: 1\n",
      "tensor([0.39210], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.761      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      3.53G    0.02845    0.02685   0.005117         27        640: 1\n",
      "tensor([0.34821], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.766      0.815       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      3.53G    0.02911     0.0274   0.005095         46        640: 1\n",
      "tensor([0.37482], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.763      0.814       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      3.53G    0.02916    0.02719   0.005207         21        640: 1\n",
      "tensor([0.21771], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.786      0.761      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      3.53G    0.02969    0.02759   0.005034         31        640: 1\n",
      "tensor([0.34205], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.764      0.814      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      3.53G    0.02914    0.02746   0.005262         38        640: 1\n",
      "tensor([0.37001], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.763      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      3.53G    0.02923    0.02733    0.00503         34        640: 1\n",
      "tensor([0.29173], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.761      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      3.53G    0.02909    0.02707   0.004953         26        640: 1\n",
      "tensor([0.26888], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.786      0.761      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      3.53G     0.0293    0.02778   0.005052         52        640: 1\n",
      "tensor([0.41874], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.762      0.814       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      3.53G    0.02902    0.02729    0.00508         35        640: 1\n",
      "tensor([0.33429], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.786      0.762      0.816      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      3.53G    0.02901    0.02724   0.005082         38        640: 1\n",
      "tensor([0.35208], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.766      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      3.53G    0.02937     0.0278   0.005029         35        640: 1\n",
      "tensor([0.41333], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.764      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      3.53G    0.02883    0.02703   0.005028         32        640: 1\n",
      "tensor([0.32849], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.762      0.816      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      3.53G    0.02924    0.02784   0.005123         56        640: 1\n",
      "tensor([0.54293], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.764      0.814      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      3.53G    0.02931    0.02704   0.005083         31        640: 1\n",
      "tensor([0.24023], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785       0.76      0.814      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      3.53G    0.02908     0.0277   0.005071         30        640: 1\n",
      "tensor([0.35447], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.761      0.814      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      3.53G    0.02909    0.02721   0.005084         35        640: 1\n",
      "tensor([0.29572], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.767      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      3.53G    0.02915    0.02724   0.004883         40        640: 1\n",
      "tensor([0.35299], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.765      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      3.53G    0.02922    0.02723   0.005128         61        640: 1\n",
      "tensor([0.43016], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.765      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      3.53G    0.02891    0.02692   0.004989         53        640: 1\n",
      "tensor([0.42437], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.762      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      3.53G    0.02918    0.02736   0.004914         57        640: 1\n",
      "tensor([0.43017], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.766      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      3.53G    0.02932    0.02727   0.005115         28        640: 1\n",
      "tensor([0.31036], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.787      0.759      0.816      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      3.53G    0.02857     0.0269   0.004937         66        640: 1\n",
      "tensor([0.51979], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778       0.77      0.814      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      3.53G     0.0292    0.02724   0.004882         45        640: 1\n",
      "tensor([0.35272], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.762      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      3.53G    0.02892     0.0268   0.004965         33        640: 1\n",
      "tensor([0.38054], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.768      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      3.53G    0.02927    0.02741   0.004947         62        640: 1\n",
      "tensor([0.45650], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.768      0.815      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      3.53G    0.02924    0.02764   0.005077         56        640: 1\n",
      "tensor([0.39373], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.763      0.814      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      3.53G    0.02895    0.02718   0.004855         58        640: 1\n",
      "tensor([0.39885], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.787      0.761      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      3.53G    0.02929    0.02744    0.00492         49        640: 1\n",
      "tensor([0.37779], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.764      0.815      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      3.53G    0.02879    0.02767   0.004793        149        640:  ^C\n",
      "      29/49      3.53G    0.02879    0.02767   0.004793        149        640:  \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/increment_VOC_Replay_base/weights/last.pt \\\n",
    "--BiC_enable  \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37f00af5-8bf2-4a38-b9cc-ba6681b71fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_BiC2/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.628       0.64      0.653      0.284\n",
      "                   car       2244       8711      0.801      0.832      0.887      0.453\n",
      "                   van       2244        861      0.557      0.692      0.629      0.286\n",
      "                 truck       2244        333      0.756      0.811      0.844      0.391\n",
      "                  tram       2244        138      0.498      0.754       0.74      0.289\n",
      "                person       2244       1286      0.664      0.625      0.655      0.271\n",
      "        person_sitting       2244         89      0.485      0.318       0.33      0.117\n",
      "               cyclist       2244        496      0.705      0.534      0.588      0.235\n",
      "                  misc       2244        284      0.554      0.556      0.554      0.227\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp177\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_BiC2/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4c5af0d-0390-4089-a084-9c536188dbd0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887df81c-0b50-472c-8efa-73ff2aca58b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a4e8964-b7da-4c86-8af6-e63287d5987f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "698e4415-a6ee-40d9-b228-e7f23248d82c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ab217e8-ff9f-4024-a99a-5f6a03e9a70d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d2c5d3d-d2b1-4501-be04-66f176d1db04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a9377cf7-651d-4ef9-bd24-9d5b18457e13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_GEM: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/temp_test.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False, GEM_enable=True\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/764c0618bfd74b70ae0d52533670bd93\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 2501 ima\u001b[0m\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "Scanning ../datasets/VOC/labels/val.cache... 1048 images, 0 backgrounds, 0 corru\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/val... 1048 images, 0 backgro\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp120/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp120\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.67G    0.08496    0.04926    0.07501         32        640: 1\n",
      "tensor([1.10564], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.157      0.159      0.072\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      3.67G    0.07533    0.05013     0.0676         14        640: 1\n",
      "tensor([0.61640], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.652      0.295      0.332      0.164\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      3.67G    0.07678    0.05165    0.06952         33        640: 1\n",
      "tensor([1.12073], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.65      0.372      0.434      0.202\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      3.67G    0.07798    0.05564     0.0709         29        640: 1\n",
      "tensor([1.00789], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.714      0.561      0.642      0.311\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      3.67G    0.07968    0.05842    0.07405         21        640: 1\n",
      "tensor([1.05353], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.593      0.663      0.657      0.344\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      3.67G    0.07818    0.06277    0.07615         21        640: 1\n",
      "tensor([1.14916], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.687      0.569      0.634      0.315\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      3.67G    0.08099    0.06391    0.07627         37        640: 1\n",
      "tensor([1.12807], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.751      0.592      0.656      0.332\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      3.67G    0.08186    0.06177    0.07965         24        640: 1\n",
      "tensor([1.23259], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.776      0.626      0.705      0.343\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      3.67G    0.07975     0.0637    0.07842         13        640: 1\n",
      "tensor([1.02333], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.685      0.676      0.716      0.368\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      3.67G    0.08073    0.06349    0.07979         22        640: 1\n",
      "tensor([0.87011], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.727      0.658      0.716      0.375\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      3.67G    0.08057    0.06372    0.08202         32        640: 1\n",
      "tensor([1.16395], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.663      0.697      0.702      0.375\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      3.67G    0.08013    0.06409    0.08228         24        640: 1\n",
      "tensor([1.21603], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.747      0.707      0.748      0.398\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      3.67G    0.07979    0.06352    0.08304         33        640: 1\n",
      "tensor([1.17374], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.748      0.717      0.753      0.408\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      3.67G    0.08053    0.06457    0.08255         15        640: 1\n",
      "tensor([0.80984], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.724      0.767      0.789      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      3.67G    0.08009    0.06548    0.08217         15        640: 1\n",
      "tensor([1.05764], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.803      0.735      0.788      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      3.67G    0.07841    0.06644    0.08622         27        640: 1\n",
      "tensor([0.99559], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.824      0.734      0.803      0.438\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      3.67G    0.07876    0.06866    0.08602         16        640: 1\n",
      "tensor([1.16771], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.737      0.817      0.437\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      3.67G    0.08121    0.06517    0.08616         31        640: 1\n",
      "tensor([1.37330], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.812      0.733      0.815      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      3.67G    0.08005    0.06762    0.08557         24        640: 1\n",
      "tensor([1.08160], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.763       0.84      0.479\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      3.67G    0.08025    0.06921    0.08764         28        640: 1\n",
      "tensor([1.42983], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.779       0.84      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      3.67G    0.08138    0.06849    0.08674          9        640: 1\n",
      "tensor([1.06663], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.816       0.79      0.847      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      3.67G    0.08102    0.07062     0.0865         37        640: 1\n",
      "tensor([1.40964], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.828      0.747       0.84      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      3.67G    0.08012    0.06851    0.08734         26        640: 1\n",
      "tensor([1.22103], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856        0.8      0.866      0.509\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      3.67G    0.07963    0.06807     0.0881         25        640: 1\n",
      "tensor([1.01307], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.815      0.826      0.842      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      3.67G    0.07898    0.06626    0.08799         30        640: 1\n",
      "tensor([1.18789], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.824      0.828      0.877      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      3.67G    0.07916    0.06633    0.08805         19        640: 1\n",
      "tensor([1.18273], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.796      0.884      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      3.67G    0.07927    0.06619    0.08761         14        640: 1\n",
      "tensor([0.86234], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.833      0.799      0.854       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      3.67G    0.07958     0.0679    0.08916         38        640: 1\n",
      "tensor([1.50711], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.903      0.825      0.906      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      3.67G    0.07938    0.06913    0.08938         20        640: 1\n",
      "tensor([1.04309], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.897       0.82      0.894       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      3.67G    0.07923    0.06977    0.09065         30        640: 1\n",
      "tensor([1.50026], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861      0.834      0.896      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      3.67G    0.07935    0.07162    0.09173         21        640: 1\n",
      "tensor([1.07596], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.822      0.904      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      3.67G    0.07926    0.06785    0.08906         29        640: 1\n",
      "tensor([1.08183], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.904      0.846      0.912       0.58\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      3.67G    0.07868     0.0701    0.08957         28        640: 1\n",
      "tensor([0.89546], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.912      0.857      0.916      0.582\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      3.67G     0.0782    0.06993    0.09024         19        640: 1\n",
      "tensor([0.92683], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.843      0.921      0.589\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      3.67G    0.07946    0.07153    0.09035         22        640: 1\n",
      "tensor([0.93632], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.852       0.92      0.584\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      3.67G    0.08009    0.07193    0.08977         32        640: 1\n",
      "tensor([1.17916], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866       0.86      0.891       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      3.67G    0.07815    0.07306    0.09033         18        640: 1\n",
      "tensor([1.14360], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.877      0.936      0.612\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      3.67G    0.07779    0.07401    0.09253         33        640: 1\n",
      "tensor([1.34432], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.874      0.925       0.59\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      3.67G    0.07816    0.07475    0.09192         38        640: 1\n",
      "tensor([1.19071], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918       0.87      0.931      0.624\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      3.67G    0.07915    0.07155    0.09185         46        640: 1\n",
      "tensor([1.38201], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.945      0.878      0.937      0.629\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      3.67G    0.07951    0.07022    0.09407         27        640: 1\n",
      "tensor([0.98472], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.882      0.936      0.628\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      3.67G    0.07975    0.07244    0.09298         18        640: 1\n",
      "tensor([1.21423], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.893      0.938      0.616\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      3.67G    0.08015    0.06951    0.09315         24        640: 1\n",
      "tensor([1.22328], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.896      0.938      0.619\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      3.67G    0.07806    0.07423    0.09251         21        640: 1\n",
      "tensor([0.99553], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.935      0.883      0.942      0.641\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      3.67G    0.07905    0.07403    0.09549         28        640: 1\n",
      "tensor([1.09989], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.945      0.896      0.948      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      3.67G    0.07998    0.07326    0.09398         34        640: 1\n",
      "tensor([1.31705], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921       0.89      0.941       0.65\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      3.67G    0.07911    0.07238    0.09533         29        640: 1\n",
      "tensor([1.37570], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.96      0.888      0.947      0.659\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      3.67G    0.07939     0.0731    0.09607         23        640: 1\n",
      "tensor([1.23652], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931       0.91      0.945      0.669\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      3.67G    0.07838    0.07447    0.09575         28        640: 1\n",
      "tensor([1.15255], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.909      0.948      0.667\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      3.67G    0.07819    0.07377    0.09541         19        640: 1\n",
      "tensor([1.03771], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.914      0.948      0.671\n",
      "\n",
      "50 epochs completed in 0.556 hours.\n",
      "Optimizer stripped from runs/train/exp120/weights/last.pt, 14.4MB\n",
      "Optimizer stripped from runs/train/exp120/weights/best.pt, 14.4MB\n",
      "\n",
      "Validating runs/train/exp120/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.913      0.948      0.671\n",
      "                   car       1048       4012      0.962      0.941      0.983      0.785\n",
      "                   van       1048        431      0.979      0.974      0.989      0.766\n",
      "                 truck       1048        166      0.978      0.976      0.991      0.777\n",
      "                  tram       1048         56      0.949      0.995      0.994      0.708\n",
      "                person       1048        618      0.932      0.751      0.873      0.501\n",
      "        person_sitting       1048         20      0.843       0.85      0.829      0.502\n",
      "               cyclist       1048        234      0.947       0.91      0.958      0.632\n",
      "                  misc       1048        138      0.866      0.906      0.965      0.696\n",
      "Results saved to \u001b[1mruns/train/exp120\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/764c0618bfd74b70ae0d52533670bd93\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                         : 0.9515039549101711\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives            : 150.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                     : 0.9834647812569777\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95                 : 0.7852759847857052\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision                  : 0.9618028092414536\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                     : 0.9414233213772097\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives             : 3777.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_f1                     : 0.9282610944238733\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_false_positives        : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_mAP@.5                 : 0.9577225756474445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_mAP@.5:.95             : 0.6323577919538537\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_precision              : 0.9469924090757424\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_recall                 : 0.9102564102564102\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_true_positives         : 213.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [1570]                    : (0.4066372513771057, 4.6992387771606445)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]          : (0.15932939961179302, 0.948428323974848)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]     : (0.07198004648461057, 0.6711276349157256)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]        : (0.5933194562193757, 0.9597962813102895)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]           : (0.15716089998612576, 0.9140626439311932)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_f1                        : 0.8852450918668896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_false_positives           : 19.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_mAP@.5                    : 0.964643385467445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_mAP@.5:.95                : 0.6961514335535315\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_precision                 : 0.8656050173052089\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_recall                    : 0.9057971014492754\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_true_positives            : 125.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                      : 0.8315527277101853\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives         : 34.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5                  : 0.8726689657688708\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95              : 0.5009498480365252\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision               : 0.931755806941055\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall                  : 0.7508090614886731\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_f1              : 0.8466204420215941\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_false_positives : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_mAP@.5          : 0.8291954410091187\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_mAP@.5:.95      : 0.5022838722111602\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_precision       : 0.8432676515278854\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_recall          : 0.85\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_true_positives  : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support                 : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives          : 464.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]           : (0.07533012330532074, 0.08495507389307022)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]           : (0.06760242581367493, 0.09607113897800446)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]           : (0.04925919696688652, 0.0747460350394249)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_f1                        : 0.9714302494070169\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_false_positives           : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_mAP@.5                    : 0.9941343825665859\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_mAP@.5:.95                : 0.7075634692545567\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_precision                 : 0.9489093374807839\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_recall                    : 0.9950461479716799\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_true_positives            : 56.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_f1                       : 0.9769010185291176\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_false_positives          : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5                   : 0.9905168565351977\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5:.95               : 0.7766742791340773\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_precision                : 0.9779004634426322\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_recall                   : 0.9759036144578314\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_true_positives           : 162.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]             : (0.024676073342561722, 0.05324345454573631)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]             : (0.001921831863000989, 0.033356308937072754)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]             : (0.03371284902095795, 0.055586960166692734)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_f1                         : 0.9767212572584109\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_false_positives            : 9.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5                     : 0.9891573149773238\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5:.95                 : 0.765951152096138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_precision                  : 0.9790200345106888\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_recall                     : 0.9744332499552917\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_true_positives             : 420.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                    : (0.0004960000000000005, 0.07019108280254777)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                    : (0.0004960000000000005, 0.009583609341825903)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                    : (0.0004960000000000005, 0.009583609341825903)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/764c0618bfd74b70ae0d52533670bd93\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BiC_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : 0.0001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp120\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (2.03 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_GEM.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/temp_test.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--GEM_enable \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c50ced2f-03c1-4022-a9ca-287d0334e8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/exp120/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.944     0.0239     0.0256     0.0133\n",
      "                   car       4952       1201      0.417      0.397      0.358      0.206\n",
      "                person       4952       4528      0.469     0.0801      0.114     0.0432\n",
      "             aeroplane       4952        285          1          0          0          0\n",
      "               bicycle       4952        337          1          0    0.00183    0.00061\n",
      "                  bird       4952        459          1          0    0.00157   0.000668\n",
      "                  boat       4952        263          1          0   0.000631   0.000208\n",
      "                bottle       4952        469          1          0    0.00335    0.00135\n",
      "                   bus       4952        213          1          0    0.00988    0.00506\n",
      "                   cat       4952        358          1          0   0.000201   8.04e-05\n",
      "                 chair       4952        756          1          0     0.0018   0.000574\n",
      "                   cow       4952        244          1          0    0.00242    0.00138\n",
      "           diningtable       4952        206          1          0          0          0\n",
      "                   dog       4952        489          1          0    0.00162    0.00105\n",
      "                 horse       4952        348          1          0    0.00206   0.000735\n",
      "             motorbike       4952        325          1          0    0.00935    0.00315\n",
      "           pottedplant       4952        480          1          0   0.000207   0.000103\n",
      "                 sheep       4952        242          1          0    0.00121    0.00056\n",
      "                  sofa       4952        239          1          0   0.000613   0.000223\n",
      "                 train       4952        282          1          0    0.00172   0.000588\n",
      "             tvmonitor       4952        308          1          0    0.00134    0.00054\n",
      "Speed: 0.1ms pre-process, 1.4ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp183\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/exp120/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# 这个新数据集上有点差。明天换5e-5跑一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40e95607-9f0c-41b7-8760-c6384bb4b0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/exp120/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.854      0.761      0.819      0.527\n",
      "                   car       2244       8711      0.887      0.903      0.944       0.71\n",
      "                   van       2244        861      0.869      0.832      0.874      0.626\n",
      "                 truck       2244        333      0.926      0.898       0.94      0.692\n",
      "                  tram       2244        138      0.826      0.906      0.902      0.561\n",
      "                person       2244       1286      0.829      0.685      0.751      0.377\n",
      "        person_sitting       2244         89      0.752      0.408      0.537      0.281\n",
      "               cyclist       2244        496      0.861      0.738      0.814      0.443\n",
      "                  misc       2244        284      0.883      0.718      0.793      0.523\n",
      "Speed: 0.0ms pre-process, 0.6ms inference, 1.3ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp184\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/exp120/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2501b5c-d08d-426d-a285-8e2a02132091",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34fd7b62-9871-427a-8316-613d3c809dc1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83e2839f-3bc2-4254-9eb2-e457cac19863",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c4ead501-9e2c-4de8-9346-da3effb2ef4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_GEM: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/temp_test.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False, GEM_enable=True\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/99ac00275fef48a69d9564dd30345f07\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 2501 ima\u001b[0m\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "Scanning ../datasets/VOC/labels/val... 1048 images, 0 backgrounds, 0 corrupt: 10\n",
      "New cache created: ../datasets/VOC/labels/val.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/val... 1048 images, 0 backgro\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp156/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp156\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.63G      0.115    0.04679     0.0823         97        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/49      3.68G    0.08264    0.04909    0.07353         32        640: 1\n",
      "tensor([0.95275], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675     0.0279      0.193     0.0331     0.0174\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      3.68G    0.05987    0.04264    0.05787         14        640: 1\n",
      "tensor([0.50614], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858     0.0969      0.106     0.0549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      3.68G    0.05536    0.03831    0.05187         33        640: 1\n",
      "tensor([0.79314], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.117      0.134     0.0606\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      3.68G    0.05118    0.03762    0.04635         71        640:  "
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_GEM.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/temp_test.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--GEM_enable \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b29381-d473-4e72-a7f8-87e69e07bf87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4020a41b-d3e6-4d63-a60f-0b9034c40027",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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